All-Range Correlation Volume.  Convolution kernels operate in a local spatial neighborhood, which limits its ability to capture long-range correspondence.
Inspired by the recent work in dense correspondence matching [1]}, we compute visual correspondence by considering pixel features in all the spatial range at a time.
Given the features of a frame pair, \(\mathcal {F}_\theta (\mathbf {I}_{i}),\,\mathcal {F}_\theta (\hat{\mathbf {I}}_{i+1})\in \mathbb {R}^{H^{\prime }\times W^{\prime }\times C}\) , a 4D correlation volume \(\mathbf {C}(\mathbf {I}_{i},\,{\hat{\mathbf {I}}_{i+1}})\in \mathbb {R}^{H^{\prime }\times W^{\prime }\times H^{\prime }\times W^{\prime }}\)  at the full resolution of the feature maps is computed as follows:
\(\leavevmode \xbox {resizebox}{\XMLaddatt {width}{0.0pt}\mathbf {C}(\mathbf {I}_{i},\,\hat{\mathbf {I}}_{i+1})_{xyuv} = \exp \Big (\sum \limits _{c}\mathcal {F}_\theta (\mathbf {I}_{i})_{xyc}\cdot \mathcal {F}_\theta (\hat{\mathbf {I}}_{i+1})_{uvc}\Big ),}\) 
